Levels: Ph.D. (prospective/current, TXST) | M.S. (prospective/current, TXST) | Outstanding Undergraduate (current TXST)
Lab: Artificial Intelligence in Transportation (AIT) Lab
Start: Flexible | Duration: 12–24 months (renewable, performance based)
The AIT Lab at Texas State University is seeking a motivated research assistant with strong foundations in artificial intelligence, and software engineering to support safety critical transportation research. Projects leverage large scale crash data, infrastructure data, multimodal mobility datasets, and AI driven decision support tools. Suitable candidates must have prior Q1 journal publications and open source contributions (1,000+ GitHub stars).
AIT Lab projects: https://lnkd.in/gqDxCMah
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Core Responsibilities
• Develop and benchmark models for crash severity, driver behavior, and infrastructure risk
• Build end to end ML pipelines: preprocessing, feature engineering, training, evaluation, deployment
• Implement explainable and trustworthy AI methods (counterfactuals, causal inference, explainable/mechanistic AI)
• Contribute to journals and conferences in transportation, AI, and data science
• Maintain reproducible, research quality codebases for long term use and open source release
• Collaborate with interdisciplinary teams across engineering, AI, and policy
Required Qualifications
• Strong in Python for deep learning
• Experience with PyTorch and/or TensorFlow
• Knowledge of statistical learning, validation, and uncertainty analysis
• Large dataset experience (SQL, Pandas, NumPy) and spatial AI
• Reproducible workflows and strong software practices (Git, documentation, experiment tracking)
Preferred Qualifications
• Prior AI or research experience (CS, statistics, transportation, related)
• Evidence of strong analytics (publications, preprints, competitions, open source)
• Ph.D.: independent methodological research capability, 2 Q1 publications, 500+ GitHub stars
• M.S./UG: excellent coursework, research mindset, and 1000+ GitHub stars
📩 To apply: Send a brief intro, CV, and (if available) links to GitHub / Google Scholar / prior work to Dr. Subasish Das: subasish@txstate.edu
